URL Phishing Detection Using Machine Learning

Gonsalves, Sconal Godfrey (2024) URL Phishing Detection Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

The overall performance of all the machine learning models on three different datasets that are been selected are examined in this research work which is been done, with keeping an aim of feature selection and the strategies of reducing the dimensionality. After thorough examination and testing of the models like Random Forest and Gradient Boosting out performed and showed a very exceptional accuracy and toughness. In order to balance the accuracy and the computational efficiency recursive Feature Elimination was used and it emerged as very effective method for doing it, while at the same time enhanced the ensemble techniques and maintained performance of the model. The main role was played by correlation matrix in improving the generalization for the models which were simpler by overcoming the problem of plurality but at the same time by overcoming that problem it created a problem of overfitting when all the features which are highly correlated were retained. The results which were achieved provides an approach which can be followed for implementing a scalable and accessible solutions using machine learning in it.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Moldovan, Arghir Nicolae UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	23 Jul 2025 13:31
Last Modified:	23 Jul 2025 13:31
URI:	https://norma.ncirl.ie/id/eprint/8210

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